A distributed data-parallel framework for analysis and visualization algorithm development

Jeremy S. Meredith, Roberto Reynel Sisneros, David Pugmire, Sean Ahern

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The coming generation of supercomputing architectures will require fundamental changes in programming models to effectively make use of the expected million to billion way concurrency and thousand-fold reduction in per-core memory. Most current parallel analysis and visualization tools achieve scalability by partitioning the data, either spatially or temporally, and running serial computational kernels on each data partition, using message passing as needed. These techniques lack the necessary level of data parallelism to execute effectively on the underlying hardware. This paper introduces a framework that enables the expression of analysis and visualization algorithms with memory-efficient execution in a hybrid distributed and data parallel manner on both multi-core and many-core processors. We demonstrate results on scientific data using CPUs and GPUs in scalable heterogeneous systems.

Original languageEnglish (US)
Title of host publication5th Workshop on General-Purpose Computation on Graphics Processing Units, GPGPU-5 - Held in Cooperation with ACM ASPLOS XVII
Pages11-19
Number of pages9
DOIs
StatePublished - Mar 28 2012
Event5th Workshop on General-Purpose Computation on Graphics Processing Units, GPGPU-5 - Held in Cooperation with ACM ASPLOS XVII - London, United Kingdom
Duration: Mar 3 2012Mar 3 2012

Publication series

NameACM International Conference Proceeding Series

Other

Other5th Workshop on General-Purpose Computation on Graphics Processing Units, GPGPU-5 - Held in Cooperation with ACM ASPLOS XVII
CountryUnited Kingdom
CityLondon
Period3/3/123/3/12

Keywords

  • GPGPU
  • analysis
  • visualization

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'A distributed data-parallel framework for analysis and visualization algorithm development'. Together they form a unique fingerprint.

Cite this